OBJECTIVES: Cardiovascular disease (CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals. METHODS: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set (n=4 524) and a validation set (n=1 131) in an 8꞉2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA). RESULTS: The mean age of participants was 52 years old. There were 3 400 males (60.12%). The overall detection rate of vascular plaque in the screening population was 49.87% (2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets (all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals (all P<0.05). The area under the curve (AUC) of the nomogram model for predicting vascular plaque risk were 0.778 (95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760 (95% CI 0.732 to 0.787, P<0.001) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated good model calibration (training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99. CONCLUSIONS The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.
Humans ; Male ; Middle Aged ; Female ; Nomograms ; Retrospective Studies ; Risk Factors ; Plaque, Atherosclerotic/diagnosis* ; Aged ; Adult ; Physical Examination ; Logistic Models ; Cardiovascular Diseases/epidemiology* ; ROC Curve

Objective:Inflammation has been confirmed to play an important role in the occurrence and development of sudden sensorineural hearing loss（SSNHL）, and the neutrophil-to-lymphocyte ratio（NLR） is a biomarker positively correlated with the degree of inflammation. This study aims to identify the difference in serum NLR between patients with SSNHL and normal population, and to evaluate the predictive efficacy of NLR for the occurrence and prognosis of SSNHL, thereby guiding the clinical diagnosis and treatment of SSNHL. Methods:In this study, 96 patients diagnosed with SSNHL admitted to our department from January 2023 to March 2024 and 96 patients diagnosed with vocal cord polyps admitted to our department during the same period were recruited as a control group. Multivariate Logistic regression was used to evaluate independent related factors, and a nomogram was constructed to predict the probability of SSNHL. The receiver operating characteristic（ROC） curve and calibration curve were used to evaluate the accuracy of prediction. Results:Multivariate logistic regression analysis showed that a high level NLR（OR2.215; 95%CI1.597-3.073; P<0.001） were independently associated with the presence of SSNHL. High age（OR1.036; 95%CI1.009-1.067; P=0.012）, high FIB（OR2.35; 95%CI1.176-4.960; P=0.019） were the risk factor for SSNHL. Incorporating these 3 factors, a forest plot and a nomogram were generated. The ROC curve, nomogram and calibration curve showed that the model had good clinical practicability. A low NLR（OR0.598; 95%CI0.439-0.816; P<0.001） was significantly associated with a favorable prognosis of SSNHL. Conclusion:Elevated NLR can serve as an promising biomarker for assessing the risk of SSNHL. The nomograms calculation model may be utilized as a tool to estimate the probability of SSNHL. Low level NLR is significantly associated with a good prognosis of SSNHL.
Humans ; Neutrophils ; Female ; Male ; Lymphocytes ; Hearing Loss, Sensorineural/blood* ; Hearing Loss, Sudden/diagnosis* ; Middle Aged ; Prognosis ; Nomograms ; ROC Curve ; Adult ; Logistic Models ; Biomarkers/blood* ; Lymphocyte Count ; Inflammation/blood* ; Clinical Relevance

Objective:To summarize and analyze the clinical characteristics of patients with sudden sensorineural hearing loss（SSHL） accompanying diabetes mellitus, to explore whether diabetes affects the treatment outcomes during hospitalization, and to identify the factors influencing the efficacy of SSHL patients with diabetes. Methods:A retrospective analysis was conducted on clinical data from 939 patients with SSHL. The baseline characteristics, and onset conditions of the diabetes group（79 cases） and the non-diabetes group（860 cases） were compared. Propensity score matching（PSM） was applied in a 1︰ 2 ratio to match initial hearing levels with baseline characteristics such as age, sex, and BMI, resulting in 73 diabetes cases and 144 non-diabetes cases for treatment efficacy comparison. For the analysis of prognostic factors, a logistic regression model was established based on the treatment outcomes of 217 patients with SSHL. Results:The proportion of SSHL patients accompanying diabetes was 8.40%（79/939）. Compared to non-diabetic patients, those with diabetes were older（median age of 53 years in the diabetes group and 39 years in the non-diabetes group） and had a higher proportion of hypertension（43.04% vs 12.67%）, with significant difference observed（P<0.05）. After PSM, the treatment efficacy during hospitalization was better in the diabetes group than in the non-diabetes group（58.90% vs 47.92%）, although the difference was not statistically significant（P>0.05）. The prognosis of patients with SSNHL accompanied by diabetes was analyzed using a multivariate logistic regression model that included age, HDL-C, and INR as variables; however, no statistically significant differences were found（P>0.05）. Conclusion:Patients with SSHL accompanying diabetes are generally older with a higher incidence of hypertension. The presence of diabetes does not affect the treatment outcomes during hospitalization.
Humans ; Propensity Score ; Retrospective Studies ; Hearing Loss, Sensorineural/therapy* ; Hearing Loss, Sudden/therapy* ; Middle Aged ; Diabetes Mellitus ; Male ; Female ; Prognosis ; Adult ; Logistic Models ; Diabetes Complications ; Aged ; Treatment Outcome

Objective:To explore the factors influencing adherence to voice therapy among patients with voice disorders in China. Methods:Patients with voice disorders who visited the Voice Therapy Center at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, from February to May 2022 were enrolled in the study. Adherence was assessed using the URICA-Voice scale, while influencing factors were assessed through the Voice Handicap Index（VHI） scale and a general information questionnaire. Correlation analysis was conducted using univariate and multivariate logistic regression analysis. Results:A total of 247 patients were included in the study, comprising 57 males（23.08%） and 190 females（76.92%）. The results revealed that: ①Female patients demonstrated higher likelihood of being in the contemplation stage（OR=0.22） compared to males. ②Patients with a monthly family income per capita>6 000 yuan were more likely to be in the contemplation stage than those with<3 000 yuan with an OR = 13.94. ③High vocal-demand occupations increased contemplation stage probability（OR=7.70） compared to moderate-demand occupations. ④Residence within 30-minute commute predicted action/maintenance stages（OR=7.14） versus≥60-minute commute. ⑤Patients whose occupations had high voice demands were more likely to be in the action and maintenance stages than those with average voice demands, with an OR of 16.20. Conclusion:Gender, monthly family income per capita, occupational voice demands, and distance to the hospital significantly impact the URICA-Voice compliance stages of patients. Patients who are female, have higher family income, have occupations with high voice demands, and live closer to the hospital exhibit higher compliance with voice training.
Humans ; Male ; Female ; Voice Disorders/therapy* ; Patient Compliance ; Voice Training ; Surveys and Questionnaires ; China ; Middle Aged ; Adult ; Voice Quality ; Logistic Models ; Aged

Objective:To explore the influencing factors related to olfactory dysfunction secondary to different types of chronic rhinosinusitis（CRS）. Methods:A retrospective analysis was conducted on 185 CRS patients treated at the Department of Otolaryngology-Head and Neck Surgery of Shanxi Provincial People's Hospital from July 2023 to July 2024. Based on the presence or absence of nasal polyps, CRS was divided into two groups: chronic rhinosinusitis with nasal polyps（CRSwNP） and chronic rhinosinusitis without nasal polyps（CRSsNP）. Further, based on whether olfactory dysfunction was present, the CRSwNP and CRSsNP groups were divided into subgroups with olfactory dysfunction and normal olfaction. General data, laboratory tests, and modified sinus CT scores were compared between the subgroups. Logistic regression analysis was conducted to identify independent influencing factors based on the results of univariate analysis combined with clinical significance, and two nomogram models were established. The area under the curve of the receiver operating characteristic（ROC） curve, calibration curves, and decision curve analysis were used to assess the diagnostic performance, calibration, and clinical utility of the predictive model. Results:The proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region were identified as independent influencing factors in the CRSwNP group; the proportion of blood monocytes and modified CT scores of the bilateral posterior region were independent influencing factors in the CRSsNP group. The nomogram prediction model showed good diagnostic performance, calibration, and clinical utility in both the CRSwNP and CRSsNP groups. Conclusion:Olfactory dysfunction in CRSwNP patients is closely related to the proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region, while olfactory dysfunction in CRSsNP patients is closely related to the proportion of blood monocytes and modified CT scores of the bilateral posterior region. Moreover, the predictive model established in this study demonstrates good clinical performance and can be used for early identification and risk prediction of olfactory dysfunction secondary to CRS.
Humans ; Sinusitis/complications* ; Chronic Disease ; Retrospective Studies ; Olfaction Disorders/etiology* ; Nasal Polyps/complications* ; Rhinitis/complications* ; Female ; Male ; Logistic Models ; Middle Aged ; Smell ; Adult ; ROC Curve ; Nomograms ; Eosinophils ; Tomography, X-Ray Computed

Objective:To investigate the sleep characteristics and clinical features of patients with vestibular migraine（VM）, and to explore the influencing factors of sleep disorder in VM patients. Methods:A cross-sectional study method was adopted to collect VM patients from Otolaryngology department and neurology department of our hospital from June 2022 to June 2024（divided into sleep disorder group and non-sleep disorder group according to whether there is sleep disorder） as the experimental group, and recruit non-VM volunteers with clinical characteristics matching with the experimental group during the same period as the control group. The clinical data of the subjects were collected, and the sleep quality of the subjects was assessed using the Pittsburgh Sleep Quality Index（PSQI）. The influencing factors of sleep disorders in VM patients were analyzed by multivariate Logistic regression, and the correlation between sleep disorders and clinical features such as headache, vertigo and hearing in VM patients was analyzed by Spearman correlation coefficient. Results:A total of 530 individuals with VM were analyzed, including 332 with sleep disturbances（62.64%）, 198 without sleep issues（37.36%）, and 50 in the control group. The overall PSQI score and all its components were significantly higher in the VM group compared with the control group（P<0.05）. A positive correlation was observed between PSQI and VAS, DHI-T, DHI-E, DHI-F and DHI-P（r=0.797, P<0.05; r=0.834, P<0.05; r=0.794, P<0.05; r=0.771, P<0.05; r=0.877, P<0.05）, PSQI had no correlation with pure tone hearing（r=0.324, P=0.167）. Multivariate logistic regression analysis showed that female, age ≥60 years, living alone, duration of disease ≥3 months, motion sickness history, and HADS-A were independent influencing factors for comorbidification of sleep disorder in VM patients（P<0.05）. Conclusion:The prevalence of sleep disorders in patients with vestibular migraine（VM） was significantly higher compared to the control group. Moreover, the severity of sleep disorders was positively correlated with the intensity of headache and vertigo in VM patients. It is recommended that female VM patients aged 60 years or older, living alone, with a disease duration of three months or longer, a history of motion sickness, and anxiety symptoms undergo sleep assessments to determine the presence of sleep disorders. This approach provides a theoretical foundation for precise treatment and prevention strategies for VM.
Humans ; Migraine Disorders/complications* ; Sleep Wake Disorders/complications* ; Cross-Sectional Studies ; Vertigo ; Female ; Male ; Vestibular Diseases/complications* ; Sleep Quality ; Adult ; Middle Aged ; Logistic Models

Objective:To explore the influencing factors of olfactory impairment in patients with obstructive sleep apnea（OSA） and establish a nomogram prediction model. Methods:A total of 100 OSA patients were enrolled. Snap&Sniff olfactory test was used to evaluate the olfactory identification function and olfactory threshold of the patients. According to the scoring criteria, either olfactory identification scores below 14 points or olfactory threshold scores below 3 points was defined as olfactory impairment. Multivariate logistic regression analysis was used to explore the influencing factors of olfactory impairment in OSA. The nomogram model was constructed by using the R 4.4.2 software package. ROC curve, calibration curve and decision curve were used to evaluate the predictive efficacy, consistency and clinical utility of the model. Results:A total of 55 of 100 OSA patients had olfactory impairment. The results of multivariate logistic regression analysis showed that age, ESS score, MoCA score, and apnea-hypopnea index（AHI） were the influencing factors of olfactory impairment in OSA. Based on the above parameters, a nomogram model was established. The ROC curve analysis showed that the AUC was 0.897（95%CI 0.834-0.961）, indicating that the model had good predictive ability. The calibration curve showed that the predicted probability of the model fits the actual probability well. Decision curve analysis showed that when the threshold probability was in the range of 0-0.9, the model had a high clinical net benefit rate. Conclusion:Age, ESS score, MoCA score and AHI are the influencing factors of olfactory impairment in patients with OSA. The nomogram model constructed based on the above factors has good predictive value, which is conducive to the clinical multi-angle understanding of OSA and the formulation of scientific prevention and treatment measures.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Nomograms ; Olfaction Disorders/etiology* ; Logistic Models ; Middle Aged ; Male ; Female ; ROC Curve ; Adult ; Aged

Objective:To investigate the clinical characteristics of residual dizziness（RD） after repositioning in patients with benign paroxysmal positional vertigo（BPPV）, identify its potential risk factors, and develop a predictive risk model. Methods:A total of 137 patients diagnosed with BPPV at the First Affiliated Hospital of Xi'an Jiaotong University between January 2023 and June 2023 were enrolled. Based on the presence or absence of subjective discomfort within 3 months after successful repositioning, patients were divided into the non-RD group（NRD, n=93） and the RD group（n=44）. Differences in demographic characteristics, comorbidities, and disease-related features were compared between groups. Multivariate logistic regression analysis was used to identify independent risk factors for RD, and a nomogram was constructed based on these factors. The predictive performance of the model was assessed using the area under the curve（AUC）. Results:The RD group showed significantly higher values in body mass index, prevalence of diabetes and motion sickness history, dizziness duration before repositioning, history of repositioning at external hospitals, number of treatments, and recurrence（all P<0.001）. Multivariate logistic regression revealed that diabetes（adjusted OR=8.73, P=0.039）, motion sickness history（adjusted OR=23.08, P<0.001）, dizziness duration ≥30 days before repositioning（adjusted OR=15.16, P<0.001）, and recurrence（adjusted OR=15.72, P=0.001） were independent risk factors for RD. The nomogram model based on these variables demonstrated good predictive ability, with an AUC of 0.804（95%CI 0.684-0.924）. Conclusion:Diabetes, motion sickness history, dizziness duration ≥30 days, and recurrence are independent risk factors for RD after repositioning in patients with BPPV. The nomogram model based on these variables shows good predictive performance, with recurrence having the highest predictive value. This model can aid in early identification of high-risk patients and guide individualized intervention strategies.
Humans ; Nomograms ; Benign Paroxysmal Positional Vertigo/therapy* ; Dizziness/etiology* ; Risk Factors ; Risk Assessment ; Multivariate Analysis ; Male ; Female ; Logistic Models ; Middle Aged ; Patient Positioning ; Adult

Objective:To explore the predictive value of preoperative peripheral hematological parameters combined with clinicopathological features for cervical lymph node metastasis（CLNM） in patients with laryngeal squamous cell carcinoma（LSCC）, and to construct and validate a nomogram model for CLNM. Methods:A retrospective analysis was conducted on the clinical data of 264 LSCC patients who underwent surgical treatment and were pathologically confirmed, collected from the Second Affiliated Hospital of Shandong First Medical University and Taian 88 Hospital. Specifically, 161 patients from one hospital were allocated to the training cohort, while 103 patients from another hospital constituted the validation cohort. Based on postoperative pathological results, patients were categorized into CLNM-positive and CLNM-negative groups. The general clinical data, clinicopathological features, and hematological parameters of the two groups were analyzed and compared. A preoperative predictive model for CLNM was developed using logistic regression analysis, followed by validation and sensitivity analysis to evaluate the robustness of the model's predictive performance. Results:The results showed that there were significant differences in tumor location, tumor size, tumor differentiation, neutrophil percentage, lymphocyte count, lymphocyte percentage, c-reactive protein（CRP）, fibrinogen, neutrophil-to-lymphocyte ratio（NLR）, platelet-to-lymphocyte ratio（PLR）, systemic immune-inflammation index（SII）, systemic inflammation response index（SIRI）, and prognostic inflammatory index（PIV） between the CLNM-positive and CLNM-negative groups（P<0.05）. Lasso regression identified tumor location, clinical T stage, tumor size, tumor differentiation degree, red blood cell distribution width（RDW） -coefficient of variation（RDW-CV）, CRP, FIB, D-dimer, NLR, and lymphocyte-to-monocyte ratio（LMR） were the most predictive parameters. Multivariate logistic regression revealed that tumor location, tumor size, tumor differentiation degree, CRP, and NLR were independent risk factors for CLNM in LSCC patients（P<0.05）. A nomogram was constructed based on these five factors. The model demonstrated excellent discrimination, with a C-index of 0.837（95%CI 0.766-0.908） in the training cohort and 0.809（95%CI 0.698-0.920） in the validation cohort. Calibration curves and DCA curves in both cohorts confirmed the clinical utility of the model. Sensitivity analysis further supported the robustness of the results, showing good discrimination and calibration across different age and BMI subgroups. Conclusion:Tumor location, tumor size, tumor differentiation degree, CRP, and NLR were independent risk factors for CLNM in LSCC patients. The nomogram based on these variables exhibits strong discrimination, calibration, and clinical applicability, and may serve as a valuable tool for preoperative risk assessment and individualized treatment planning.
Humans ; Nomograms ; Laryngeal Neoplasms/blood* ; Retrospective Studies ; Lymphatic Metastasis ; Carcinoma, Squamous Cell/blood* ; Lymph Nodes/pathology* ; Male ; Female ; Middle Aged ; Neck ; C-Reactive Protein ; Aged ; Logistic Models ; Neutrophils ; Prognosis

Objective:To investigate the risk factors of wet ear status and its impact on the efficacy of endoscopic type Ⅰ tympanoplasty. Methods:A retrospective analysis was conducted at the Department of Otolaryngology Head and Neck Surgery, the First Affiliated Hospital of Chinese People's Liberation Army（PLA）Air Force Medical University, on 160 ears that underwent endoscopic type Ⅰ tympanoplasty; these were assigned to a dry-ear group （n= 118） and a wet-ear group （n= 42）.Univariate analysis and binary logistic regression were used to identify risk factors for wet ear status. Postoperative outcomes, including tympanic meoombrane healing rate and hearing improvement across frequencies, were compared between groups. Results:①Significant intergroup differences were observed in age, residual tympanic membrane status, external auditory canal condition, mastoid pneumatization（MC0）, and middle ear ventilation dysfunction（P<0.05）; ②The degree of mastoid pneumatization being MC0 is an independent risk factor for wet ear（P<0.05）; ③No significant difference in tympanic membrane healing rates was found（P>0.05）; ④The wet ear group showed significantly higher pre-and postoperative air-conduction（AC） and bone-conduction（BC） thresholds at 2 kHz and 4 kHz compared to the dry ear group（P<0.05）, though the postoperative air-bone gap（ABG） improvement was comparable. Conclusion:Poor mastoid pneumatization is a risk factor for wet ears. The wet ear state has no effect on tympanic membrane healing and air-bone conduction gap, but patients in the wet ear group may have more severe inner ear or auditory nerve pathway damage.
Humans ; Retrospective Studies ; Tympanoplasty/methods* ; Adult ; Risk Factors ; Male ; Female ; Young Adult ; Endoscopy ; Adolescent ; Middle Aged ; Treatment Outcome ; Child ; Logistic Models ; Tympanic Membrane/surgery*